{"id":13574,"date":"2018-04-18T09:07:43","date_gmt":"2018-04-18T09:07:43","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=13574"},"modified":"2023-08-17T20:35:25","modified_gmt":"2023-08-17T15:05:25","slug":"pig-advantages-and-disadvantages","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pig-advantages-and-disadvantages\/","title":{"rendered":"Apache Pig Advantages and Disadvantages"},"content":{"rendered":"

As we all know, we use Apache Pig<\/strong> to analyze large sets of data, as well as to represent them as data flows. However, Pig attains many more advantages in it. In the same place, there are some disadvantages also. <\/span><\/p>\n

So, in this article “Pig Advantages and Disadvantages”, we will discuss all the advantages as well as disadvantages of Apache Pig in detail.<\/span><\/p>\n

What is Apache Pig?<\/span><\/h3>\n

Apache Pig is nothing but a data flow language. It is built on top of Hadoop. Basically, without having to write vanilla MapReduce <\/strong>jobs, it makes easier to process, clean and analyze “Big Data<\/strong>” in\u00a0Hadoop<\/strong>.<\/p>\n

In addition, it has a lot of relational database features. Moreover, commands like good old joins, distinct, union and many more are already in the language.<\/p>\n

To be specific, Pig solves differently than the relational database, so it is applicable to “big data” where it can crunch large files with ease and it does not need a structured data.<\/p>\n

Also, we can use Pig for ETL(Extraction Transformation Load) tasks naturally as it can handle unstructured data. Now, let’s jump to Pig advantages and disadvantages.<\/p>\n

Apache Pig Advantages and Disadvantages
\n<\/span><\/h3>\n

Like every coin has two faces, which show its strength and weakness. So, let\u2019s discuss both Apache Pig Pros and Cons individually:<\/p>\n

Advantages of Apache Pig<\/span><\/h3>\n

First, let’s check the benefits of Apache Pig –<\/p>\n

i. Less development time<\/h4>\n

It consumes less time while development. Hence, we can say, it is one of the major advantages. Especially considering vanilla MapReduce\u00a0<\/strong>jobs’ complexity, time-spent, and maintenance of the programs.<\/span><\/p>\n

ii. Easy to learn<\/h4>\n

Well, the Learning curve of Apache Pig is not steep. That implies anyone who does not know how to write vanilla MapReduce or SQL<\/strong> for that matter could pick up and can write MapReduce jobs.<\/span><\/p>\n

iii. Procedural language<\/h4>\n

Apache Pig is a procedural language, not declarative, unlike SQL. Hence, we can easily follow the commands. Also, offers better expressiveness in the transformation of data in every step. <\/span><\/p>\n

Moreover, while we compare it to vanilla MapReduce, it is much more like the English language. In addition, it is very concise and unlike Java but more like Python<\/strong>.<\/span><\/p>\n

iv. Dataflow<\/h4>\n

It is a data flow language. That means here everything is about data even though we sacrifice control structures like for loop or if structures. By “this data and because of data”, data transformation is a first class citizen. <\/span><\/p>\n

Also, we cannot create for loops without data. We need to always transform and manipulate data.<\/span><\/p>\n

v.\u00a0<\/span>Easy to control execution<\/h4>\n

We can control the execution<\/strong> of every step because it is procedural in nature. Also, a benefit that it is, straightforward. That implies we can write our own UDF(User Defined Function) and inject in one specific part in the pipeline.<\/span><\/p>\n

vi. UDFs<\/h4>\n

It is possible to write our own UDFs<\/strong>.\u00a0\u00a0<\/span><\/p>\n

vii. Lazy evaluation<\/h4>\n

As per its name, it does not get evaluated unless you do not produce an output file or does not output any message. It is a benefit of the logical plan. That it could optimize the program beginning to end and optimizer could produce an efficient plan to execute.<\/span><\/p>\n

viii. Usage of Hadoop features<\/h4>\n

Through Pig, we can enjoy everything that Hadoop offers<\/strong>. Such as parallelization, fault-tolerance with many relational database features.<\/span><\/p>\n

ix. Effective for unstructured<\/h4>\n

Pig is quite effective for unstructured and messy large datasets. Basically, Pig is one of the best tools to make the large unstructured data to structured.<\/span>
\n<\/span><\/p>\n

x. Base pipeline<\/h4>\n

Here, we have UDFs which we want to parallelize and utilize for large amounts of data. That means we can use Pig as a base pipeline where it does the hard work. For that, we just apply our UDF in the step that we want.<\/span><\/p>\n

11. Abstraction of Complex MapReduce Code:<\/h4>\n

Pig offers a greater degree of abstraction than MapReduce, allowing users to create data processing jobs in a more comprehensible and succinct scripting language (Pig Latin). Pig also provides a higher level of abstraction than MapReduce. By eliminating the need to write low-level MapReduce code, this abstraction speeds up development and increases developer accessibility.<\/p>\n

12. Extensibility:<\/h4>\n

Pig’s capabilities may be expanded by users by creating User-Defined Functions (UDFs) in Java, Python, or another supported language. With the help of this capability, users may easily add their own data processing logic to Pig scripts.<\/p>\n

b. Limitations of Apache Pig<\/span><\/h3>\n

Now, have a look at Apache Pig disadvantages –<\/p>\n

    \n
  1. Errors of Pig<\/li>\n
  2. Not mature<\/li>\n
  3. Support<\/li>\n
  4. Minor one<\/li>\n
  5. Implicit data schema<\/li>\n
  6. Delay in execution<\/li>\n<\/ol>\n

    i. Errors of Pig<\/h4>\n

    Errors that Pig produces due to UDFs(Python) are not helpful at all. At times, while something goes wrong, it just gives the error such as exec error in UDF, even if the problem is related to syntax or the type error, it lets alone a logical one.<\/span><\/p>\n

    ii. Not mature<\/h4>\n

    Pig is still in the development, even if it has been around for quite some time.<\/span><\/p>\n

    iii. Support<\/h4>\n

    Generally, Google and StackOverflow do not lead good solutions for the problems.<\/span><\/p>\n

    iv. Implicit data schema<\/h4>\n

    In Apache Pig, Data Schema is not enforced explicitly but implicitly. It is also a huge disadvantage.\u00a0As it does not enforce an explicit schema, sometimes one data structure goes byte array, which is a \u201craw\u201d data type. <\/span><\/p>\n

    It is up to the time we coerce the fields even the strings, they turn byte array without notice. It leads to propagation for other steps of the data processing.<\/span><\/p>\n

    v. Minor one<\/h4>\n

    Here is an absence of good IDE or plugin for Vim. That offers more functionality than syntax completion to write the pig scripts.<\/span><\/p>\n

    vi. Delay in execution<\/h4>\n

    Unless either we dump or store an intermediate or final result the commands are not executed. This increases the iteration between debug and resolve the issue.<\/span><\/p>\n

    So, this was all on Pig Advantages and Disadvantages.<\/p>\n

    Conclusion<\/span><\/h3>\n

    As a result, we have seen all the Pig advantages and disadvantages. However, if any doubt occurs, feel free to ask in the comment section.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    As we all know, we use Apache Pig to analyze large sets of data, as well as to represent them as data flows. However, Pig attains many more advantages in it. 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